Developers building AI applications are not just looking for fault patterns after deployment; they must detect issues quickly during development and have the ability to prevent issues after going live. Unfortunately, traditional observability tools can no longer meet the needs of AI-driven enterprise application development. AI-powered detection and auto-remediation tools designed to keep pace with rapid development are now emerging to proactively manage performance and prevent downtime ...

PostgreSQL promises greater flexibility, performance, and cost savings compared to proprietary alternatives. But successfully deploying it isn't always straightforward, and there are some hidden traps along the way that even seasoned IT leaders can stumble into. In this blog, I'll highlight five of the most common pitfalls with PostgreSQL deployment and offer guidance on how to avoid them, along with the best path forward ...

Governments and social platforms face an escalating challenge: hyperrealistic synthetic media now spreads faster than legacy moderation systems can react. From pandemic-related conspiracies to manipulated election content, disinformation has moved beyond "false text" into the realm of convincing audiovisual deception ...

Traditional monitoring often stops at uptime and server health without any integrated insights. Cross-platform observability covers not just infrastructure telemetry but also client-side behavior, distributed service interactions, and the contextual data that connects them. Emerging technologies like OpenTelemetry, eBPF, and AI-driven anomaly detection have made this vision more achievable, but only if organizations ground their observability strategy in well-defined pillars. Here are the five foundational pillars of cross-platform observability that modern engineering teams should focus on for seamless platform performance ...

A recent MIT study reveals that 95% of enterprise AI solutions fail, with 85% of AI project failures attributed to data readiness issues ... The reality is stark: AI effectiveness depends primarily on data quality, and organizations consistently struggle with data discovery, access, quality, structure, readiness, security, and governance. These challenges demand expert solutions, yet they often receive less attention than the flashy "AI will change everything" narratives that dominate industry discourse ...

Your company's knowledge base is the engine that will power your AI agents. It's the structured data repository that an AI system uses to understand, reason, and make decisions ... Thus, transferring accurate knowledge is crucial. Incomplete or poor-quality data can reduce agent performance, accuracy, and even increase agent bias. Here are five steps to ensuring that your company's knowledge base transfer is optimal ...

Most teams collect observability data for the obvious reasons: uptime, latency, troubleshooting. It's the stuff we have to do to keep the lights on. But that mindset limits what this data is really capable of. When we treat logs like a transient utility instead of a long-term resource, we end up throwing away insight we can't get back. Losing that data isn't just a technical issue; it limits your ability to make smarter business decisions ...

Enterprises are racing to leverage AI in their database environments — but most are skipping the guardrails. According to Quest research, 67% of organizations say AI is already critical to their database operations. Yet fewer than half report having a formal governance framework in place to manage it. That mismatch puts businesses at risk — operationally, financially, and reputationally ...

If you've worked in infrastructure or observability engineering for more than a few years, chances are you've been told more than once that practices like sampling, data aggregation, and short retention windows are just "best practices." The rationale is familiar: save money, reduce system strain, stay agile. But I want to challenge that framing. These aren't best practices. They're coping mechanisms. And they're costing us more than we realize ...

We're inching ever closer toward a long-held goal: technology infrastructure that is so automated that it can protect itself. But as IT leaders aggressively employ automation across our enterprises, we need to continuously reassess what AI is ready to manage autonomously and what can not yet be trusted to algorithms ...

Much like a traditional factory turns raw materials into finished products, the AI factory turns vast datasets into actionable business outcomes through advanced models, inferences, and automation. From the earliest data inputs to the final token output, this process must be reliable, repeatable, and scalable. That requires industrializing the way AI is developed, deployed, and managed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

Today, organizations are generating and processing more data than ever before. From training AI models to running complex analytics, massive datasets have become the backbone of innovation. However, as businesses embrace the cloud for its scalability and flexibility, a new challenge arises: managing the soaring costs of storing and processing this data ...

Real privacy protection thanks to technology and processes is often portrayed as too hard and too costly to implement. So the most common strategy is to do as little as possible just to conform to formal requirements of current and incoming regulations. This is a missed opportunity ...

The expanding use of AI is driving enterprise interest in data operations (DataOps) to orchestrate data integration and processing and improve data quality and validity, according to a new report from Information Services Group (ISG) ...

Lose your data and the best case scenario is, well, you know the word — but at worst, it is game over. And so World Backup Day has traditionally carried a very simple yet powerful message for businesses: Backup. Your. Data ...

Image

Are they simply number crunchers confined to back-office support, or are they the strategic influencers shaping the future of your enterprise? The reality is that data analysts are far more the latter. In fact, 94% of analysts agree their role is pivotal to making high-level business decisions, proving that they are becoming indispensable partners in shaping strategy ...

Gartner identified the top data and analytics (D&A) trends for 2025 that are driving the emergence of a wide range of challenges, including organizational and human issues ...

A recent Rocket Software and Foundry study found that just 28% of organizations fully leverage their mainframe data, a concerning statistic given its critical role in powering AI models, predictive analytics, and informed decision-making ...

In today’s data and AI driven world, enterprises across industries are utilizing AI to invent new business models, reimagine business and achieve efficiency in operations. However, enterprises may face challenges like flawed or biased AI decisions, sensitive data breaches and rising regulatory risks ...

In today's data-driven world, the management of databases has become increasingly complex and critical. The following are findings from Redgate's 2025 The State of the Database Landscape report ...

The pressure on IT teams has never been greater. As data environments grow increasingly complex, resource shortages are emerging as a major obstacle for IT leaders striving to meet the demands of modern infrastructure management ... According to DataStrike's newly released 2025 Data Infrastructure Survey Report, more than half (54%) of IT leaders cite resource limitations as a top challenge, highlighting a growing trend toward outsourcing as a solution ...

Image